arXiv (All)
open access
The Advantages of Fresh Sketching for Ridge Regression
Over the past 25 years, sketching and sampling have become widely used tools for accelerating large-scale regression. In iterative randomized solvers, a basic design choice is whether to $\textit{reuse}$ the same sketch or draw $\textit{fresh}$ randomness at every step. For (under-constrained) itera
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Bootstrap Error Estimation and Sketch-Size Selection for Sketched Ridge Regression#616641
- Bootstrap Error Estimation and Sketch-Size Selection for Sketched Ridge Regression#616141
- Global Linear Convergence of the Proximal Bundle Method under Unknown Piecewise Smoothness and Quadratic Growth#999009
- Exact hierarchical algorithms for accelerating particle-mesh coupling in sparse-grid particle-in-cell methods#471976
- Optimal Scaling of Langevin Proposals with Generalized Acceptance Rules#1123351
Record · ID 1143166
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive